Learning Logic Program Representation for Delayed Systems With Limited Training Data. - Yin Jun Phua; Tony Ribeiro; Sophie Tourret; Katsumi Inoue (ILP (Late Breaking Papers), 2017)

Learning Logic Programs from Noisy State Transition Data. - Yin Jun Phua; Katsumi Inoue (ILP, 2019)

Learning Representation of Relational Dynamics with Delays and Refining with Prior Knowledge. - Yin Jun Phua; Tony Ribeiro; Katsumi Inoue (FLAP, 2019)

Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance. - Yin Jun Phua; Katsumi Inoue (ILP, 2021)

Class-Incremental Learning using Diffusion Model for Distillation and Replay. - Quentin Jodelet; Xin Liu 020; Yin Jun Phua; Tsuyoshi Murata (ICCV (Workshops), 2023)

Variable Assignment Invariant Neural Networks for Learning Logic Programs. - Yin Jun Phua; Katsumi Inoue (NeSy (1), 2024)

DEGNN: Dual Experts Graph Neural Network Handling both Edge and Node Feature Noise. - Tai Hasegawa; Sukwon Yun; Xin Liu 020; Yin Jun Phua; Tsuyoshi Murata (PAKDD (2), 2024)

Memory augmented using diffusion model for class-incremental learning. - Quentin Jodelet; Xin Liu 020; Yin Jun Phua; Tsuyoshi Murata (Image Vis. Comput., 2025)

Future-proofing class-incremental learning. - Quentin Jodelet; Xin Liu 020; Yin Jun Phua; Tsuyoshi Murata (Mach. Vis. Appl., 2025)